Adaptive variable neighbourhood search approach for time-dependent joint location and dispatching problem in a multi-tier ambulance system

Nadar, Raviarun A.; Jha, J.K.; Thakkar, Jitesh J.

doi:10.1016/j.cor.2023.106355

Cited by 5 publications

(2 citation statements)

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“…The main contribution of this research is the consideration of relocating temporary ambulance stations as the demand rate changes. The work of [28] analyzed the problem of ambulance location-allocation considering the station preference order to dispatch ambulances, the temporal variation in demand and travel time, the probabilities of station-specific ambulance occupancy, and possible everyday ambulance relocation. Their proposal used a particle swarm approach to obtain the number of ambulances assigned to each station.…”

Section: Related Workmentioning

confidence: 99%

“…The adaptive VNS metaheuristic was used to solve the problem. However, both [27,28] recommended for future work to extend model experiments with different travel times from temporary stations to demand points, considering different times of day, since in a disaster situation the closest temporary station will not always be the first to arrive. To address this issue, the model proposed in the present paper consulted the traffic data corresponding to the time of day of each scenario analyzed.…”

Section: Related Workmentioning

confidence: 99%

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Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%